Design patterns for AI agents acting across multi-step tasks, covering how tool access, memory, orchestration topology, and coordination overhead shape whether an agent system works in practice.

AI coding assistants accelerate development but introduce tradeoffs around skill atrophy, codebase design, verification, and security that shape how much value they actually deliver.

Developer tooling spans shell ergonomics, CI infrastructure, type-safe validation, test analytics, and AI-assisted automation, with sources collectively showing that the best tools reduce friction and surface failures earlier without adding their own failure modes.

LLM orchestration coordinates multiple language model agents through structured pipelines, separating planning, generation, and evaluation roles to produce reliable outputs from long-running autonomous tasks.

LLM-based multi-agent systems coordinate multiple AI agents on decomposed tasks, but empirical work shows failure rates of 41–87%, with information synthesis rather than coordination being the core bottleneck.